Value of predictive bioinformatics in inherited ...

WJ MG

World Journal of Medical Genetics World J Med Genet 2015 August 27; 5(3): 46-51 ISSN 2220-3184 (online)

Submit a Manuscript: http://www.wjgnet.com/esps/ Help Desk: http://www.wjgnet.com/esps/helpdesk.aspx DOI: 10.5496/wjmg.v5.i3.46

© 2015 Baishideng Publishing Group Inc. All rights reserved.

EDITORIAL

Value of predictive bioinformatics in inherited metabolic diseases David J Timson the majority do not. It is now well accepted that the majority of these disease-associated mutations exert their effects through alteration of protein stability, which causes a reduction in enzymatic activity. This finding suggests a way to predict the severity of newly discovered mutations. In silico prediction of the effects of amino acid sequence alterations on protein stability often correlates with disease severity. However, no stability prediction tool is perfect and, in general, better results are obtained if the predictions from a variety of tools are combined and then interpreted. In addition to predicted alterations to stability, the degree of conservation of a particular residue can also be a factor which needs to be taken into account: alterations to highly conserved residues are more likely to be associated with severe forms of the disease. The approach has been successfully applied in a variety of inherited metabolic diseases, but further improvements are necessary to enable robust translation into clinically useful tools.

David J Timson, School of Biological Sciences and Institute for Global Food Security, Queen’s University Belfast, BT9 7BL Belfast, United Kingdom Author contributions: Timson DJ conceived and wrote the paper Conflict-of-interest statement: The author has no conflicts of interest to declare. Open-Access: This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/ licenses/by-nc/4.0/ Correspondence to: Dr. David J Timson, School of Biological Sciences and Institute for Global Food Security, Queen’s University Belfast, 97 Lisburn Road, BT9 7BL Belfast, United Kingdom. [email protected] Telephone: +44-028-90975875 Fax: +44-028-90975877

Key words: Genetic disease; Metabolism; In silico method; Protein stability; Disease-associated mutation © The Author(s) 2015. Published by Baishideng Publishing Group Inc. All rights reserved.

Received: February 27, 2015 Peer-review started: March 2, 2015 First decision: April 27, 2015 Revised: April 28, 2015 Accepted: May 16, 2015 Article in press: May 18, 2015 Published online: August 27, 2015

Core tip: Bioinformatics and other in silico methods are increasingly being used to predict the severity of disease-associated mutations in inherited metabolic diseases. In general, severity correlates with altered protein stability and the best predictions occur when a variety of tools are applied.

Abstract

Timson DJ. Value of predictive bioinformatics in inherited metabolic diseases. World J Med Genet 2015; 5(3): 46-51 Available from: URL: http://www.wjgnet.com/2220-3184/full/ v5/i3/46.htm DOI: http://dx.doi.org/10.5496/wjmg.v5.i3.46

Typically, inherited metabolic diseases arise from point mutations in genes encoding metabolic enzymes. Although some of these mutations directly affect amino acid residues in the active sites of these enzymes,

WJMG|www.wjgnet.com

46

August 27, 2015|Volume 5|Issue 3|

Timson DJ. Predictive bioinformatics in inherited metabolic diseases include the patient’s diet, activity levels and access to good quality medical care. However, the most important factor in determining the severity of symptoms is normally the exact mutation(s) that the patient has. Most inherited metabolic diseases are not caused by one, single mutation. The majority have many possible mutations which are associated with the disease. For example, there are almost 250 mutations in galactose 1-phosphate uridylyltransferase which are associated [19,20] with type Ⅰ galactosemia . Since these mutations alter different amino acid residues, it follows that they will have different effects on the protein. Some will have relatively minor effects on the protein’s overall structure and stability whereas others may render the protein essentially non-functional. Novel, disease-associated mutations continue to be discovered. Indeed, with the decreasing price of whole exome sequencing, we should expect that the rate of discovery of novel mutations will increase in the next [21,22] few years . In some inherited metabolic diseases problems are apparent within a few days of birth; however, in other cases, babies are born with near normal physiology but progressively decline over the following years. Given the range of possible severities associated with some inherited metabolic diseases it is a challenge to physicians and scientists to predict the likely symptoms of an individual patient and to plan treatment accordingly. This is particularly the case for newly discovered disease-associated mutations.

INTRODUCTION Inherited metabolic diseases result from mutations in the genes encoding enzymes involved in intermediary metabolism. Well characterised examples include galacto­ semia, lysosomal storage diseases and phenylketonuria. Typically these diseases manifest with effects at the whole organism level, despite their origins at the metabolic pathway level. Physical and cognitive dis­ abilities are associated with many inherited metabolic diseases. While individual diseases are generally rare, the cumulative effect of many of these diseases has [1-4] a significant effect on societies and economies . Furthermore, the burdens on patients, their families and their immediate communities can be devastating since many of these diseases result in progressive deterioration of the patient resulting, in some cases, in death in childhood or early adulthood. Very few of these diseases have effective therapies (i.e., treatments which restore normal, or near-normal, functioning to the patient). One barrier to the development of therapies is the rareness of the diseases: there is limited incentive to the development of drugs or other treatments which would only be applicable to a small number of patients [2,5,6] worldwide . Where therapies do exist, they tend to be extremely expensive, often exceeding United States [7,8] $100000 per patient per year (for example, see ). Biochemical studies on the underlying molecular pathology of a range of inherited metabolic diseases have revealed some common themes. In particular, mutations associated with these diseases often cause changes which destabilise the corresponding protein [9-15] (for examples, see ). Very few disease-associated mutations directly affect the residues in the active site of the enzyme; the majority affect residues elsewhere in the protein. A common molecular mechanism of disease causation is that the altered amino acid residue causes a global reduction in the enzyme’s stability [16] resulting in reduced catalytic activity . The loss of stability can also be associated with reduced affinity for essential cofactors or increased aggregation of the partially folded protein. It is, of course, the loss of enzymatic activity which commonly leads to disease, for example by reducing the amount of product made or causing a build-up of toxic intermediates. In other cases the accumulation of aggregated protein results in a breakdown of cellular homeostasis. Nevertheless, partial protein misfolding lies at the base of these problems and is the fundamental cause of the disease in these cases. It is also apparent that, in many inherited metabolic diseases, there is a range of possible symptoms. This is particularly stark in diseases like type Ⅲ galactosemia and mevalonate kinase deficiency. In these diseases the manifestations range from near-normal physiology with some alterations in blood chemistry to highly disabling, [17,18] . The experience of each life-threatening conditions patient will be determined by his/her genetic back­ ground, lifestyle and environment. Critical elements

WJMG|www.wjgnet.com

THE CHALLENGE OF PREDICTION The link between protein stability and severity of disease suggests a way in which predictions might be made. There are a variety of software packages, many freely available online, which claim to predict the stability of proteins (Table 1). In theory if a range of disease-associated variant proteins are compared to the wild-type in one of these packages then the greater the predicted instability, the more severe the disease is likely to be. In practice the situation is more complex. No prediction software is 100% accurate and different packages can give different results for the same variant. This problem can be overcome by using a variety of different programs and aggregating the results together to obtain a consensus. However, problems still remain. In some diseases, both decreases and increases in stability can be associated with disease (for examples [9,23] see ). Indeed, it appears that as well as an optimum structure for activity, proteins also require an optimal degree of flexibility and stability. Protein flexibility is [24] inextricably linked to ligand binding and catalysis . Thus, increased stability can lead to a more rigidified, less flexible protein which is less able to bind substrates and catalyse the reaction. While protein stability is undoubtedly a key factor in disease causation, there are other factors which need to be considered. Alterations in key residues involved in binding substrates or in the chemistry of catalysis will

47

August 27, 2015|Volume 5|Issue 3|

Timson DJ. Predictive bioinformatics in inherited metabolic diseases Table 1 Examples of freely available, online tools for predicting the properties of variant proteins Category Structural analysis

Stability prediction

Binding affinity changes Aggregation tendency, amyloid formation and chaperone binding

Sequence conservation

Name

Weblink

Ref.

YASARA energy minimisation LS-SNP GETAREA I-Mutant 3.0 mCSM SDM score Mupro iStable PredictSNP 1.0 Meta-SNP KD4V Fold-X PoPMuSiC CUPSAT GETAREA BeAtMuSiC TANGO

www.yasara.org/minimizationserver.htm ls-snp.icm.jhu.edu/ls-snp-pdb/main curie.utmb.edu/getarea.html gpcr2.biocomp.unibo.it/cgi/predictors/I-Mutant3.0/I-Mutant3.0.cgi bleoberis.bioc.cam.ac.uk/mcsm/ mordred.bioc.cam.ac.uk/~sdm/sdm.php mupro.proteomics.ics.uci.edu predictor.nchu.edu.tw/iStable/ loschmidt.chemi.muni.cz/predictsnp/ snps.biofold.org/meta-snp/ decrypthon.igbmc.fr/kd4v foldx.crg.es dezyme.com/ cupsat.tu-bs.de curie.utmb.edu/getarea.html babylone.ulb.ac.be/beatmusic tango.crg.es/

[29] [30] [31] [32,33] [34] [35,36] [37] [38] [39] [40] [41] [42] [43] [44,45] [31] [46] [47]

WALTZ LIMBO Clustal Omega Scorecons SIFT PROVEAN LS-SNP SNPs and GO PANTHER GenMAPP PolyPhen 2 nsSNP Analyzer FI mutation assessor YALE MU2A

www.switchlab.org/bioinformatics/waltz www.switchlab.org/bioinformatics/limbo www.ebi.ac.uk/Tools/msa/clustalo/ www.ebi.ac.uk/thornton-srv/databases/cgi-bin/valdar/scorecons_server.pl sift.jcvi.org/ provean.jcvi.org/index.php ls-snp.icm.jhu.edu/ls-snp-pdb/ snps.biofold.org/snps-and-go/pages/help.html www.pantherdb.org/tools/csnpScoreForm.jsp www.genmapp.org genetics.bwh.harvard.edu/pph2/ snpanalyzer.uthsc.edu mutationassessor.org/v1 krauthammerlab.med.yale.edu/mu2a

[48] [49] [50] [51] [52] [53] [30] [54] [55] [56] [57,58] [59] [60] [61]

- most importantly an experimental demonstration that protein misfolding is an important factor in disease causation. Using the known variants, a range of pre­ diction tools are applied and the combination which best predicts the known outcomes are then selected. This can then be applied to uncharacterised mutations or to polymorphisms identified through genome and exome sequencing projects. In general, the severity of disease correlates with the predicted loss of stability of the protein. The degree of conservation of the residue(s) affected is also important in some conditions (Table 2). Most studies employ a range of different prediction tools and aggregate results together to make informed [28] predictions (for example see ).

lead to direct loss of activity. A failure to interact with cellular chaperones may impede folding. Disruptions to other protein-protein interactions may also affect the enzyme’s function. Residues involved in catalysis and protein-protein interactions are generally well conserved through evolution. Therefore, we might expect that mutations which alter highly conserved residues might also lead to more severe forms of disease. Therefore, many predictions incorporate measures of sequence conservation and propensity to interact with cellular chaperones (Table 1). Overall, it is accepted that the best predictions will result from using a variety of different software packages which address different [25-27] aspects of the protein’s structure and function . Furthermore, any output requires intelligent and critical analysis by the users.

CONCLUSION AND FUTURE PERSPECTIVES

APPLICATION TO INHERITED METABOLIC DISEASES

To date, no prediction protocol has achieved complete accuracy and it is unlikely that physicians would be confident to rely on them to guide treatment of their patients. In addition, the prediction protocols published so far mostly require extensive bioinformatics analysis using a number of different tools on separate websites. Ideally these would be integrated into a single webbased package which enabled the user to submit a

These approaches have been employed in a number of inherited metabolic diseases (Table 2). Typically, a set of known mutations and their associated protein variants are identified from the literature and classified according to their association with different severities of the disease. Other information from the literature is required

WJMG|www.wjgnet.com

48

August 27, 2015|Volume 5|Issue 3|

Timson DJ. Predictive bioinformatics in inherited metabolic diseases Table 2 Examples of bioinformatics based predictions of the severity of variants associated with inherited metabolic diseases Disease Alkaptonuria Apparent mineralocorticoid excess Fabry disease

Protein

Comments

Ref.

Homogentisate 1,2-dioxygenase 11βHSD2

Combining a variety of computational approaches gave rise to the most accurate predictions

[62]

The predicted degree of structural change in the enzyme correlates with disease severity

[63]

GLA

A purpose built program designed to detect protein instability outperformed existing, generic tools A purpose built web interface allows prediction of a patient’s responsiveness to pharmacological chaperone therapy Slightly different results were obtained with different programs; however, 22 out of 47 variants were predicted to be harmful by all seven programs used A combination of prediction tools suggested that protein stability is an important factor in this disease; novel potentially disease-associated variants were identified Mutations affect residues in the active site, or protein stability, or quaternary structure Variations which decrease protein stability and/or occur in highly conserved regions of the protein are associated with disease The majority of disease-associated variants were predicted to be less stable than wild-type Protein stability predicted to be most important factor in disease causation A combination of prediction tools suggested that protein stability is an important factor in this disease; novel potentially disease-associated variants were identified Main predicted effect is the loss of stability of GALT Effects on protein stability and degree of sequence conservation combined were required for good predictions

[64]

Fabry disease

GLA

Gaucher disease

GBA

Glucose 6-phosphate dehydrogenase deficiency Hyperargininemia MODY 2 Niemann-pick disease type C Phenylketonuria Pyruvate kinase deficiency Type Ⅰ galactosemia Type Ⅲ galactosemia

G6PDH ARG1 GCK NPC1 and NPC2 PAH PK1 and PK2 GALT GALE

[65] [28] [66] [67] [68] [69] [10] [66] [70] [71]

11βHSD2: 11β-hydroxysteroid dehydrogenase type 2; GLA: α-galactosidase A; GBA: Glucocerebrosidase; G6PDH: Glucose 6-phosphate dehydrogenase; ARG1: Arginase 1; GCK: Glucokinase; PAH: Phenylalanine hydroxylase; PK1 and PK2: Pyruvate kinase isoforms 1 and 2; GALT: Galactose 1-phosphate uridylyltransferase; GALE: UDP-galactose 4’-epimerase; MODY 2: Maturity-onset diabetes of the young, type 2.

novel variant and receive a prediction. This would require the software to submit the new variant to the various online tools and integrate the responses into a single prediction. This, in turn, requires the software tools to remain “live” and at the same, stable url something which cannot be guaranteed when each tool is provided by different organisations. One solution would be to use the source code for the prediction tools directly, but this is not always freely available. Nevertheless carefully designed prediction methods can already provide some guidance in the assessment of novel mutations and have also had some success in classifying mutations identified through genome and exome sequencing projects. Interestingly, these tend to suggest that there are a number of mutations in the human population which were not previously associated with disease. In the case of type Ⅲ galactosemia, several of these were predicted to be associated with severe forms of the disease. Further experimental testing will be required to see if these predictions are valid. In conclusion, the best predictions will be made when a variety of tools are used and where they are supported by strong experimental and clinical studies. It is highly unlikely that bioinformatics predictions will completely supplant experimental studies; however, with continuing improvements in the tools used they have the potential to be a useful tool for both clinicians and scientists who work on inherited metabolic diseases.

field.

REFERENCES 1 2

3 4 5

6

7

8

ACKNOWLEDGMENTS I wish to thank Dr. Thomas McCorvie (University of Oxford, United Kingdom) for introducing me to this

WJMG|www.wjgnet.com

9

49

Angelis A, Tordrup D, Kanavos P. Socio-economic burden of rare diseases: A systematic review of cost of illness evidence. Health Policy 2015; 119: 964-979 [PMID: 25661982] Kontoghiorghe CN, Andreou N, Constantinou K, Kontoghiorghes GJ. World health dilemmas: Orphan and rare diseases, orphan drugs and orphan patients. World J Methodol 2014; 4: 163-188 [PMID: 25332915 DOI: 10.5662/wjm.v4.i3.163] López-Bastida J, Oliva-Moreno J. Cost of illness and economic evaluation in rare diseases. Adv Exp Med Biol 2010; 686: 273-282 [PMID: 20824451 DOI: 10.1007/978-90-481-9485-8_16] Pampols T. Inherited metabolic rare disease. Adv Exp Med Biol 2010; 686: 397-431 [PMID: 20824458 DOI: 10.1007/978-90-4819485-8_23] Denis A, Mergaert L, Fostier C, Cleemput I, Simoens S. A comparative study of European rare disease and orphan drug markets. Health Policy 2010; 97: 173-179 [PMID: 20800761 DOI: 10.1016/j.healthpol.2010.05.017] Heemstra HE, van Weely S, Büller HA, Leufkens HG, de Vrueh RL. Translation of rare disease research into orphan drug development: disease matters. Drug Discov Today 2009; 14: 1166-1173 [PMID: 19818412 DOI: 10.1016/j.drudis.2009.09.008] Connock M, Burls A, Frew E, Fry-Smith A, Juarez-Garcia A, McCabe C, Wailoo A, Abrams K, Cooper N, Sutton A, O’Hagan A, Moore D. The clinical effectiveness and cost-effectiveness of enzyme replacement therapy for Gaucher’s disease: a systematic review. Health Technol Assess 2006; 10: iii-iv, ix-136 [PMID: 16796930 DOI: 10.3310/hta10240] Kanters TA, Hoogenboom-Plug I, Rutten-Van Mölken MP, Redekop WK, van der Ploeg AT, Hakkaart L. Cost-effectiveness of enzyme replacement therapy with alglucosidase alfa in classicinfantile patients with Pompe disease. Orphanet J Rare Dis 2014; 9: 75 [PMID: 24884717 DOI: 10.1186/1750-1172-9-75] McCorvie TJ, Gleason TJ, Fridovich-Keil JL, Timson DJ. Mis­ folding of galactose 1-phosphate uridylyltransferase can result in

August 27, 2015|Volume 5|Issue 3|

Timson DJ. Predictive bioinformatics in inherited metabolic diseases

10

11 12

13

14

15

16 17 18

19

20

21

22

23

24 25

26

type I galactosemia. Biochim Biophys Acta 2013; 1832: 1279-1293 [PMID: 23583749 DOI: 10.1016/j.bbadis.2013.04.004] Pey AL, Stricher F, Serrano L, Martinez A. Predicted effects of missense mutations on native-state stability account for phenotypic outcome in phenylketonuria, a paradigm of misfolding diseases. Am J Hum Genet 2007; 81: 1006-1024 [PMID: 17924342 DOI: 10.1086/521879] Shi Z, Sellers J, Moult J. Protein stability and in vivo concentration of missense mutations in phenylalanine hydroxylase. Proteins 2012; 80: 61-70 [PMID: 21953985 DOI: 10.1002/prot.23159] Pey AL, Maggi M, Valentini G. Insights into human phospho­ glycerate kinase 1 deficiency as a conformational disease from biochemical, biophysical, and in vitro expression analyses. J Inherit Metab Dis 2014; 37: 909-916 [PMID: 24838780 DOI: 10.1007/s10545-014-9721-8] Pey AL, Padín-Gonzalez E, Mesa-Torres N, Timson DJ. The metastability of human UDP-galactose 4’-epimerase (GALE) is increased by variants associated with type III galactosemia but decreased by substrate and cofactor binding. Arch Biochem Biophys 2014; 562: 103-114 [PMID: 25150110] Mesa-Torres N, Fabelo-Rosa I, Riverol D, Yunta C, Albert A, Salido E, Pey AL. The role of protein denaturation energetics and molecular chaperones in the aggregation and mistargeting of mutants causing primary hyperoxaluria type I. PLoS One 2013; 8: e71963 [PMID: 24205397 DOI: 10.1371/journal.pone.0071963] Henriques BJ, Bross P, Gomes CM. Mutational hotspots in electron transfer flavoprotein underlie defective folding and function in multiple acyl-CoA dehydrogenase deficiency. Biochim Biophys Acta 2010; 1802: 1070-1077 [PMID: 20674745 DOI: 10.1016/j.bbadis.2010.07.015] Yue P, Li Z, Moult J. Loss of protein structure stability as a major causative factor in monogenic disease. J Mol Biol 2005; 353: 459-473 [PMID: 16169011 DOI: 10.1016/j.jmb.2005.08.020] Timson DJ. The structural and molecular biology of type III galactosemia. IUBMB Life 2006; 58: 83-89 [PMID: 16611573] van der Burgh R, Ter Haar NM, Boes ML, Frenkel J. Mevalonate kinase deficiency, a metabolic autoinflammatory disease. Clin Immunol 2013; 147: 197-206 [PMID: 23110805 DOI: 10.1016/ j.clim.2012.09.011] d’Acierno A, Facchiano A, Marabotti A. GALT protein database, a bioinformatics resource for the management and analysis of structural features of a galactosemia-related protein and its mutants. Genomics Proteomics Bioinformatics 2009; 7: 71-76 [PMID: 19591794 DOI: 10.1016/S1672-0229(08)60035-2] d’Acierno A, Facchiano A, Marabotti A. GALT protein database: querying structural and functional features of GALT enzyme. Hum Mutat 2014; 35: 1060-1067 [PMID: 24990533 DOI: 10.1002/ humu.22613] Boycott KM, Vanstone MR, Bulman DE, MacKenzie AE. Raredisease genetics in the era of next-generation sequencing: discovery to translation. Nat Rev Genet 2013; 14: 681-691 [PMID: 23999272 DOI: 10.1038/nrg3555] Danielsson K, Mun LJ, Lordemann A, Mao J, Lin CH. Nextgeneration sequencing applied to rare diseases genomics. Expert Rev Mol Diagn 2014; 14: 469-487 [PMID: 24702023 DOI: 10.158 6/14737159.2014.904749] Wohlers TM, Fridovich-Keil JL. Studies of the V94M-substituted human UDPgalactose-4-epimerase enzyme associated with generalized epimerase-deficiency galactosaemia. J Inherit Metab Dis 2000; 23: 713-729 [PMID: 11117433 DOI: 10.1023/­A:1005682913784] Goodey NM, Benkovic SJ. Allosteric regulation and catalysis emerge via a common route. Nat Chem Biol 2008; 4: 474-482 [PMID: 18641628 DOI: 10.1038/nchembio.98] Frédéric MY, Lalande M, Boileau C, Hamroun D, Claustres M, Béroud C, Collod-Béroud G. UMD-predictor, a new prediction tool for nucleotide substitution pathogenicity -- application to four genes: FBN1, FBN2, TGFBR1, and TGFBR2. Hum Mutat 2009; 30: 952-959 [PMID: 19370756 DOI: 10.1002/humu.20970] McGee TL, Seyedahmadi BJ, Sweeney MO, Dryja TP, Berson EL. Novel mutations in the long isoform of the USH2A gene in patients with Usher syndrome type II or non-syndromic retinitis

WJMG|www.wjgnet.com

27

28

29

30

31

32

33

34

35

36

37 38 39

40

41

42

50

pigmentosa. J Med Genet 2010; 47: 499-506 [PMID: 20507924 DOI: 10.1136/jmg.2009.075143] Bergman JE, Janssen N, van der Sloot AM, de Walle HE, Schoots J, Rendtorff ND, Tranebjaerg L, Hoefsloot LH, van RavenswaaijArts CM, Hofstra RM. A novel classification system to predict the pathogenic effects of CHD7 missense variants in CHARGE syndrome. Hum Mutat 2012; 33: 1251-1260 [PMID: 22539353 DOI: 10.1002/humu.22106] Manickam M, Ravanan P, Singh P, Talwar P. In silico identi­ fication of genetic variants in glucocerebrosidase (GBA) gene involved in Gaucher’s disease using multiple software tools. Front Genet 2014; 5: 148 [PMID: 24904648 DOI: 10.3389/­fgene.2014.0 0148] Krieger E, Joo K, Lee J, Lee J, Raman S, Thompson J, Tyka M, Baker D, Karplus K. Improving physical realism, stereochemistry, and side-chain accuracy in homology modeling: Four approaches that performed well in CASP8. Proteins 2009; 77 Suppl 9: 114-122 [PMID: 19768677 DOI: 10.1002/prot.22570] Ryan M, Diekhans M, Lien S, Liu Y, Karchin R. LS-SNP/PDB: annotated non-synonymous SNPs mapped to Protein Data Bank structures. Bioinformatics 2009; 25: 1431-1432 [PMID: 19369493 DOI: 10.1093/bioinformatics/btp242] Fraczkiewicz R, Braun W. Exact and efficient analytical calculation of the accessible surface areas and their gradients for macromolecules. J Comput Chem 1998; 19: 319-333 [DOI: 10.1002/(SICI)1096987X(199802)19:33.0.CO;2-W] Capriotti E, Fariselli P, Casadio R. I-Mutant2.0: predicting stability changes upon mutation from the protein sequence or structure. Nucleic Acids Res 2005; 33: W306-W310 [PMID: 15980478 DOI: 10.1093/nar/gki375] Capriotti E, Calabrese R, Casadio R. Predicting the insurgence of human genetic diseases associated to single point protein mutations with support vector machines and evolutionary information. Bioinformatics 2006; 22: 2729-2734 [PMID: 16895930 DOI: 10.1093/bioinformatics/btl423] Pires DE, Ascher DB, Blundell TL. mCSM: predicting the effects of mutations in proteins using graph-based signatures. Bioinformatics 2014; 30: 335-342 [PMID: 24281696 DOI: 10.1093/bioinformatics/btt691] Worth CL, Preissner R, Blundell TL. SDM--a server for predicting effects of mutations on protein stability and malfunction. Nucleic Acids Res 2011; 39: W215-W222 [PMID: 21593128 DOI: 10.1093/nar/gkr363] Worth CL, Bickerton GR, Schreyer A, Forman JR, Cheng TM, Lee S, Gong S, Burke DF, Blundell TL. A structural bioinformatics approach to the analysis of nonsynonymous single nucleotide polymorphisms (nsSNPs) and their relation to disease. J Bioinform Comput Biol 2007; 5: 1297-1318 [PMID: 18172930] Cheng J, Randall A, Baldi P. Prediction of protein stability changes for single-site mutations using support vector machines. Proteins 2006; 62: 1125-1132 [PMID: 16372356 DOI: 10.1002/prot.20810] Chen CW, Lin J, Chu YW. iStable: off-the-shelf predictor integration for predicting protein stability changes. BMC Bioinformatics 2013; 14 Suppl 2: S5 [PMID: 23369171 DOI: 10.1186/1471-2105-14-S2-S5] Bendl J, Stourac J, Salanda O, Pavelka A, Wieben ED, Zendulka J, Brezovsky J, Damborsky J. PredictSNP: robust and accurate consensus classifier for prediction of disease-related mutations. PLoS Comput Biol 2014; 10: e1003440 [PMID: 24453961 DOI: 10.1371/journal.pcbi.1003440] Capriotti E, Altman RB, Bromberg Y. Collective judgment predicts disease-associated single nucleotide variants. BMC Genomics 2013; 14 Suppl 3: S2 [PMID: 23819846 DOI: 10.1186/1471-2164-14-S3S2] Luu TD, Rusu A, Walter V, Linard B, Poidevin L, Ripp R, Moulinier L, Muller J, Raffelsberger W, Wicker N, Lecompte O, Thompson JD, Poch O, Nguyen H. KD4v: Comprehensible Knowledge Discovery System for Missense Variant. Nucleic Acids Res 2012; 40: W71-W75 [PMID: 22641855 DOI: 10.1093/nar/ gks474] Schymkowitz J, Borg J, Stricher F, Nys R, Rousseau F, Serrano L. The FoldX web server: an online force field. Nucleic Acids

August 27, 2015|Volume 5|Issue 3|

Timson DJ. Predictive bioinformatics in inherited metabolic diseases

43

44 45

46

47

48

49

50

51 52

53

54

55

56

57

Res 2005; 33: W382-W388 [PMID: 15980494 DOI: 10.1093/nar/ gki387] Dehouck Y, Kwasigroch JM, Gilis D, Rooman M. PoPMuSiC 2.1: a web server for the estimation of protein stability changes upon mutation and sequence optimality. BMC Bioinformatics 2011; 12: 151 [PMID: 21569468 DOI: 10.1186/1471-2105-12-151] Parthiban V, Gromiha MM, Schomburg D. CUPSAT: prediction of protein stability upon point mutations. Nucleic Acids Res 2006; 34: W239-W242 [PMID: 16845001 DOI: 10.1093/nar/gkl190] Parthiban V, Gromiha MM, Abhinandan M, Schomburg D. Computational modeling of protein mutant stability: analysis and optimization of statistical potentials and structural features reveal insights into prediction model development. BMC Struct Biol 2007; 7: 54 [PMID: 17705837] Dehouck Y, Kwasigroch JM, Rooman M, Gilis D. BeAtMuSiC: Prediction of changes in protein-protein binding affinity on mutations. Nucleic Acids Res 2013; 41: W333-W339 [PMID: 23723246 DOI: 10.1093/nar/gkt450] Fernandez-Escamilla AM, Rousseau F, Schymkowitz J, Serrano L. Prediction of sequence-dependent and mutational effects on the aggregation of peptides and proteins. Nat Biotechnol 2004; 22: 1302-1306 [PMID: 15361882 DOI: 10.1038/nbt1012] Maurer-Stroh S, Debulpaep M, Kuemmerer N, Lopez de la Paz M, Martins IC, Reumers J, Morris KL, Copland A, Serpell L, Serrano L, Schymkowitz JW, Rousseau F. Exploring the sequence determinants of amyloid structure using position-specific scoring matrices. Nat Methods 2010; 7: 237-242 [PMID: 20154676 DOI: 10.1038/nmeth.1432] Van Durme J, Maurer-Stroh S, Gallardo R, Wilkinson H, Rousseau F, Schymkowitz J. Accurate prediction of DnaK-peptide binding via homology modelling and experimental data. PLoS Comput Biol 2009; 5: e1000475 [PMID: 19696878 DOI: 10.1371/ journal.pcbi.1000475] Sievers F, Wilm A, Dineen D, Gibson TJ, Karplus K, Li W, Lopez R, McWilliam H, Remmert M, Söding J, Thompson JD, Higgins DG. Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol Syst Biol 2011; 7: 539 [PMID: 21988835 DOI: 10.1038/msb.2011.75] Valdar WS. Scoring residue conservation. Proteins 2002; 48: 227-241 [PMID: 12112692 DOI: 10.1002/prot.10146] Kumar P, Henikoff S, Ng PC. Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat Protoc 2009; 4: 1073-1081 [PMID: 19561590 DOI: 10.1038/nprot.2009.86] Choi Y, Sims GE, Murphy S, Miller JR, Chan AP. Predicting the functional effect of amino acid substitutions and indels. PLoS One 2012; 7: e46688 [PMID: 23056405 DOI: 10.1371/journal. pone.0046688] Calabrese R, Capriotti E, Fariselli P, Martelli PL, Casadio R. Functional annotations improve the predictive score of human disease-related mutations in proteins. Hum Mutat 2009; 30: 1237-1244 [PMID: 19514061 DOI: 10.1002/humu.21047] Brunham LR, Singaraja RR, Pape TD, Kejariwal A, Thomas PD, Hayden MR. Accurate prediction of the functional significance of single nucleotide polymorphisms and mutations in the ABCA1 gene. PLoS Genet 2005; 1: e83 [PMID: 16429166 DOI: 10.1371/ journal.pgen.0010083] Salomonis N, Hanspers K, Zambon AC, Vranizan K, Lawlor SC, Dahlquist KD, Doniger SW, Stuart J, Conklin BR, Pico AR. GenMAPP 2: new features and resources for pathway analysis. BMC Bioinformatics 2007; 8: 217 [PMID: 17588266] Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova

58

59 60 61

62

63

64

65

66

67

68

69

70

71

A, Bork P, Kondrashov AS, Sunyaev SR. A method and server for predicting damaging missense mutations. Nat Methods 2010; 7: 248-249 [PMID: 20354512 DOI: 10.1038/nmeth0410-248] Adzhubei I, Jordan DM, Sunyaev SR. Predicting functional effect of human missense mutations using PolyPhen-2. Curr Protoc Hum Genet 2013; Chapter 7: Unit7.20 [PMID: 23315928 DOI: 10.1002/0471142905.hg0720s76] Bao L, Zhou M, Cui Y. nsSNPAnalyzer: identifying diseaseassociated nonsynonymous single nucleotide polymorphisms. Nucleic Acids Res 2005; 33: W480-W482 [PMID: 15980516] Reva B, Antipin Y, Sander C. Predicting the functional impact of protein mutations: application to cancer genomics. Nucleic Acids Res 2011; 39: e118 [PMID: 21727090 DOI: 10.1093/nar/gkr407] Garla V, Kong Y, Szpakowski S, Krauthammer M. MU2A-reconciling the genome and transcriptome to determine the effects of base substitutions. Bioinformatics 2011; 27: 416-418 [PMID: 21149339 DOI: 10.1093/bioinformatics/btq658] Magesh R, George Priya Doss C. Computational methods to work as first-pass filter in deleterious SNP analysis of alkaptonuria. ScientificWorldJournal 2012; 2012: 738423 [PMID: 22606059 DOI: 10.1100/2012/738423] Manning JR, Bailey MA, Soares DC, Dunbar DR, Mullins JJ. In silico structure-function analysis of pathological variation in the HSD11B2 gene sequence. Physiol Genomics 2010; 42: 319-330 [PMID: 20571110 DOI: 10.1152/physiolgenomics.00053.2010] Riera C, Lois S, Domínguez C, Fernandez-Cadenas I, Montaner J, Rodríguez-Sureda V, de la Cruz X. Molecular damage in Fabry disease: characterization and prediction of alpha-galactosidase A pathological mutations. Proteins 2015; 83: 91-104 [PMID: 25382311 DOI: 10.1002/prot.24708] Cammisa M, Correra A, Andreotti G, Cubellis MV. Fabry_CEP: a tool to identify Fabry mutations responsive to pharmacological chaperones. Orphanet J Rare Dis 2013; 8: 111 [PMID: 23883437 DOI: 10.1186/1750-1172-8-111] B R, C GP. Path to facilitate the prediction of functional amino acid substitutions in red blood cell disorders--a computational approach. PLoS One 2011; 6: e24607 [PMID: 21931771 DOI: 10.1371/journal.pone.0024607] Carvalho DR, Brand GD, Brum JM, Takata RI, Speck-Martins CE, Pratesi R. Analysis of novel ARG1 mutations causing hyperar­ gininemia and correlation with arginase I activity in erythrocytes. Gene 2012; 509: 124-130 [PMID: 22959135 DOI: 10.1016/ j.gene.2012.08.003] George DC, Chakraborty C, Haneef SA, Nagasundaram N, Chen L, Zhu H. Evolution- and structure-based computational strategy reveals the impact of deleterious missense mutations on MODY 2 (maturity-onset diabetes of the young, type 2). Theranostics 2014; 4: 366-385 [PMID: 24578721 DOI: 10.7150/thno.7473] Fancello T, Dardis A, Rosano C, Tarugi P, Tappino B, Zampieri S, Pinotti E, Corsolini F, Fecarotta S, D’Amico A, Di Rocco M, Uziel G, Calandra S, Bembi B, Filocamo M. Molecular analysis of NPC1 and NPC2 gene in 34 Niemann-Pick C Italian patients: identification and structural modeling of novel mutations. Neurogenetics 2009; 10: 229-239 [PMID: 19252935 DOI: 10.1007/­s10048-009-0175-3] Facchiano A, Marabotti A. Analysis of galactosemia-linked muta­ tions of GALT enzyme using a computational biology approach. Protein Eng Des Sel 2010; 23: 103-113 [PMID: 20008339 DOI: 10.1093/protein/gzp076] McCorvie TJ, Timson DJ. In silico prediction of the effects of mutations in the human UDP-galactose 4’-epimerase gene: towards a predictive framework for type III galactosemia. Gene 2013; 524: 95-104 [PMID: 23644136 DOI: 10.1016/j.gene.2013.04.061] P- Reviewer: McEachin RC, Wei QZ S- Editor: Ji FF L- Editor: A E- Editor: Liu SQ

WJMG|www.wjgnet.com

51

August 27, 2015|Volume 5|Issue 3|

Published by Baishideng Publishing Group Inc 8226 Regency Drive, Pleasanton, CA 94588, USA Telephone: +1-925-223-8242 Fax: +1-925-223-8243 E-mail: [email protected] Help Desk: http://www.wjgnet.com/esps/helpdesk.aspx http://www.wjgnet.com

© 2015 Baishideng Publishing Group Inc. All rights reserved.